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DEEPSIGN: A DEEP-LEARNING ARCHITECTURE FOR SIGN LANGUAGE
(2018-11-13)
Sign languages are used by deaf people for communication. In sign languages, humans use hand gestures, body, facial expressions and movements to convey meaning. Humans can easily learn and understand sign languages, but ...
Region Based Convolutional Neural Networks for object detection and recognition in ADAS application
(2018-02-15)
Object Detection and Recognition using Computer Vision has been a very interesting and a challenging field of study from past three decades. Recent advancements in Deep Learning and as well as increase in computational ...
CLASSIFICATION OF CLINICAL NARRATIVES USING CONVOLUTIONAL NEURAL NETWORK
(2018-12-10)
Patient safety is a key aspect for good consumer care. When an individual is hospitalized or receives medication the family wants the patient safety to be above all factors. For instance, a drug can do both either cure the ...
CELL SEGMENTATION IN CANCER HISTOPATHOLOGY IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
(2016-12-07)
Cancer, the second most dreadful disease causing large scale deaths in humans is characterized by uncontrolled growth of cells in the human body and the ability of those cells to migrate from the original site and spread ...
CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS FOR PEDESTRIAN DETECTION
(2016-12-06)
Pedestrian Detection in real time has become an interesting and a challenging problem lately. With the advent of autonomous vehicles and intelligent traffic monitoring systems, more time and money are being invested into ...
ADAPTIVE ACTIVATIONS AND SHIFT INVARIANCE IN SHALLOW CONVOLUTIONAL NEURAL NETWORKS
(2021-08-13)
Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge success with resnet structures having ...
Multiscale Modeling and Simulation of Clutter in ISAR Imaging
(2020-05-05)
Clutter is common in applications of radar imaging and can adversely impact target imaging by contributing scattered energy that is not accounted for in target signal models. One potential source of clutter is moving ...
Semi-supervised Learning using Triple-Siamese Network
(2020-06-04)
Missing data problem is inevitable in mostly all research areas including Artificial Intelligence, Machine Learning and Computer Vision where we have modicum knowledge about the complete dataset. One of the key reasons of ...